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Raster-based Approach

This page explains how to use DamageScanner with raster-based exposure and hazard data. This approach is especially useful when working with gridded exposure datasets, such as population grids, land use rasters, or economic value rasters.

When to Use Raster-Based Workflows

Scenario Raster-Based Vector-Based
Gridded exposure data (e.g. land cover) ✅ ❌
Building footprints or linear infrastructure ❌ ✅
Country- or global-scale risk analysis ✅ ✅
Exposure not tied to individual assets ✅ ❌

Required Inputs

You still need the same four key inputs as explained in the Overview:

  1. Hazard raster (e.g. flood depth, wind speed)
  2. Exposure raster (e.g. land use, population density)
  3. Vulnerability curves (CSV or DataFrame)
  4. Maximum damage values (CSV, dict, or DataFrame)

⚠️ Ensure both hazard and exposure rasters are in the same CRS and aligned spatially (same resolution and extent) for optimal performance.

Minimal Working Example

from damagescanner import DamageScanner

hazard = "data/hazard_flood_depth.tif"
feature_data = "data/exposure_population_density.tif"
curves = "data/vulnerability_curves.csv"
maxdam = "data/maxdam.csv"

scanner = DamageScanner(hazard, feature_data, curves, maxdam)
damage = scanner.calculate()

This returns a GeoDataFrame where each grid cell contains an estimated direct damage value.

Notes on Raster Behavior

  • Exposure values are interpreted as damageable units per cell (e.g., people, € value, or m²)
  • Output damage is computed as:

\ exposure * damage_fraction * max_damage

  • The damage fraction is determined from the vulnerability curve, based on the hazard intensity in the same raster cell.

Common Pitfalls

⚠️ Misaligned Rasters — Make sure both rasters have the same extent, resolution, and CRS. You can use rasterio.warp or gdalwarp to resample.

⚠️ Missing Data — NoData values can propagate through your analysis. Clean them or mask them before calculation.

⚠️ Unit mismatch — Hazard units and curve x-axis must match exactly (e.g., meters, m/s).

📚 See Also